


'''simple docstring'''


import argparse
import math
import os

import torch
from neural_compressor.utils.pytorch import load
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, StableDiffusionPipeline, UNetaDConditionModel







def      snake_case_  (    ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= argparse.ArgumentParser()
				parser.add_argument(
				    """-m"""				, """--pretrained_model_name_or_path"""				, type=SCREAMING_SNAKE_CASE__				, default=SCREAMING_SNAKE_CASE__				, required=SCREAMING_SNAKE_CASE__				, help="""Path to pretrained model or model identifier from huggingface.co/models."""				, )
				parser.add_argument(
				    """-c"""				, """--caption"""				, type=SCREAMING_SNAKE_CASE__				, default="""robotic cat with wings"""				, help="""Text used to generate images."""				, )
				parser.add_argument(
				    """-n"""				, """--images_num"""				, type=SCREAMING_SNAKE_CASE__				, default=4				, help="""How much images to generate."""				, )
				parser.add_argument(
				    """-s"""				, """--seed"""				, type=SCREAMING_SNAKE_CASE__				, default=42				, help="""Seed for random process."""				, )
				parser.add_argument(
				    """-ci"""				, """--cuda_id"""				, type=SCREAMING_SNAKE_CASE__				, default=0				, help="""cuda_id."""				, )
				_SCREAMING_SNAKE_CASE					:			Tuple							= parser.parse_args()
				return args







def      snake_case_  (    SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				if not len(SCREAMING_SNAKE_CASE__	) == rows * cols:
								raise ValueError("""The specified number of rows and columns are not correct."""	)

				_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE					:			List[str]							= imgs[0].size
				_SCREAMING_SNAKE_CASE					:			List[str]							= Image.new("""RGB"""				, size=(cols * w, rows * h)	)
				_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE					:			List[Any]							= grid.size

				for i, img in enumerate(SCREAMING_SNAKE_CASE__	):
								grid.paste(SCREAMING_SNAKE_CASE__				, box=(i % cols * w, i // cols * h)	)
				return grid







def      snake_case_  (    SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__="robotic cat with wings"				, SCREAMING_SNAKE_CASE__=7.5				, SCREAMING_SNAKE_CASE__=50				, SCREAMING_SNAKE_CASE__=1				, SCREAMING_SNAKE_CASE__=42				, ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= torch.Generator(pipeline.device	).manual_seed(SCREAMING_SNAKE_CASE__	)
				_SCREAMING_SNAKE_CASE					:			List[str]							= pipeline(
				    SCREAMING_SNAKE_CASE__				, guidance_scale=SCREAMING_SNAKE_CASE__				, num_inference_steps=SCREAMING_SNAKE_CASE__				, generator=SCREAMING_SNAKE_CASE__				, num_images_per_prompt=SCREAMING_SNAKE_CASE__				, ).images
				_SCREAMING_SNAKE_CASE					:			Tuple							= int(math.sqrt(SCREAMING_SNAKE_CASE__	)	)
				_SCREAMING_SNAKE_CASE					:			Dict							= image_grid(SCREAMING_SNAKE_CASE__				, rows=_rows				, cols=num_images_per_prompt // _rows	)
				return grid, images


UpperCAmelCase_						:  Dict				  =						parse_args()
# Load models and create wrapper for stable diffusion
UpperCAmelCase_						:  str				  =						CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder='tokenizer')
UpperCAmelCase_						:  List[Any]				  =						CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder='text_encoder')
UpperCAmelCase_						:  Optional[int]				  =						AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder='vae')
UpperCAmelCase_						:  str				  =						UNetaDConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder='unet')

UpperCAmelCase_						:  str				  =						StableDiffusionPipeline.from_pretrained(
    args.pretrained_model_name_or_path, text_encoder=text_encoder, vae=vae, unet=unet, tokenizer=tokenizer
)
UpperCAmelCase_						:  Optional[int]				  =						lambda images, clip_input: (images, False)
if os.path.exists(os.path.join(args.pretrained_model_name_or_path, 'best_model.pt')):
				UpperCAmelCase_						:  Dict				  =						load(args.pretrained_model_name_or_path, model=unet)
				unet.eval()
				setattr(pipeline, 'unet', unet)
else:
				UpperCAmelCase_						:  str				  =						unet.to(torch.device('cuda', args.cuda_id))
UpperCAmelCase_						:  Optional[int]				  =						pipeline.to(unet.device)
UpperCAmelCase_  ,			UpperCAmelCase_						:  Union[str, Any]				  =						generate_images(pipeline, prompt=args.caption, num_images_per_prompt=args.images_num, seed=args.seed)
grid.save(os.path.join(args.pretrained_model_name_or_path, '{}.png'.format('_'.join(args.caption.split()))))
UpperCAmelCase_						:  Tuple				  =						os.path.join(args.pretrained_model_name_or_path, '_'.join(args.caption.split()))
os.makedirs(dirname, exist_ok=True)
for idx, image in enumerate(images):
				image.save(os.path.join(dirname, '{}.png'.format(idx + 1)))








'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase_						:  int				  =						{'configuration_wavlm': ['WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'WavLMConfig']}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCAmelCase_						:  Tuple				  =						[
				    'WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'WavLMForAudioFrameClassification',
				    'WavLMForCTC',
				    'WavLMForSequenceClassification',
				    'WavLMForXVector',
				    'WavLMModel',
				    'WavLMPreTrainedModel',
				]

if TYPE_CHECKING:
				from .configuration_wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_wavlm import (
								    WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST,
								    WavLMForAudioFrameClassification,
								    WavLMForCTC,
								    WavLMForSequenceClassification,
								    WavLMForXVector,
								    WavLMModel,
								    WavLMPreTrainedModel,
								)

else:
				import sys

				UpperCAmelCase_						:  Any				  =						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




